Dynamic allocation of resources in surge demand

ABSTRACT

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for optimized resource transformation given a set of resource optimization parameters. In accordance with one embodiment, a method is provided that includes: identifying a demand surge scenario associated with resource demand conditions; in response to identifying the scenario: determining a downgrade set of resources; determining whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set satisfies the conditions; and responsive to determining the downgrade-only resource transformation scenario fails to satisfy the conditions: identifying residual resources that are transformable to meet the conditions, processing the residual resources using a machine learning model characterized by the set of resource optimization parameters to generate resource priority scores, and generating a optimized resource transformation scenario from a scenario based at least in part on the resource priority scores and the downgrade-only resource transformation scenario.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/057,385, filed Jul. 28, 2020, which isincorporated herein by reference in its entirety, including any figures,tables, and drawings.

TECHNOLOGICAL FIELD

Embodiments of the present disclosure generally relate to systems andmethods for systematically and proactively recommending and executingthe transformation of resources to satisfy resource demand conditionsresulting from specific resource demand surge scenarios.

BACKGROUND

A need exists in the industry to address technical challenges related tosystematically and proactively recommending transformation of resourcesto satisfy resource demand conditions resulting from specific resourcedemand surge scenarios. The disclosed techniques can be used inexecuting automated actions to transform resources in an efficient andcost effective manner to meet demand surge scenarios. It is with respectto these considerations and others that the disclosure herein ispresented.

BRIEF SUMMARY

In general, embodiments of the present disclosure provide methods,apparatus, systems, computing devices, computing entities, and/or thelike for optimized resource transformation given a set of resourceoptimization parameters. In accordance with one aspect of thedisclosure, a method for optimized resource transformation given a setof resource optimization parameters is provided. In various embodiments,the method includes: identifying a demand surge scenario, wherein thedemand surge scenario is associated with one or more resource demandconditions; in response to identifying the demand surge scenario:determining, based at least in part on a plurality of resourcetransformation data objects associated with a plurality of resources, adowngrade set of the plurality of resources; determining whether adowngrade-only resource transformation scenario characterized bydowngrade transformation of the downgrade set of the plurality ofresources satisfies the one or more resource demand conditions; inresponse to determining that the downgrade-only resource transformationscenario satisfies the one or more resource demand conditions,generating an optimized resource transformation scenario based at leastin part on the downgrade-only resource transformation scenario; and inresponse to determining that the downgrade-only resource transformationscenario fails to satisfy the one or more resource demand conditions:(i) identifying one or more residual resources of the plurality ofresources that are deemed transformable to meet the one or more resourcedemand conditions but are not in the downgrade set, (ii) processing theone or more residual resources using a resource optimization machinelearning model that is characterized by the set of resource optimizationparameters to generate one or more resource priority scores for the oneor more residual resources, (iii) generating a hybrid resourcetransformation scenario based at least in part on the one or moreresource priority scores and the downgrade-only resource transformationscenario, and (iv) generating the optimized resource transformationscenario based at least in part on the hybrid resource transformationscenario; and causing one or more resource transformation actions to beperformed based at least in part on the optimized resourcetransformation scenario.

In accordance with another aspect of the present disclosure, anapparatus is provided. In various embodiments, the apparatus includes atleast one processor and at least one memory including program code. Theat least one memory and the program code are configured to, with the atleast one processor, cause the apparatus to at least: identify a demandsurge scenario, wherein the demand surge scenario is associated with oneor more resource demand conditions; in response to identifying thedemand surge scenario: determine, based at least in part on a pluralityof resource transformation data objects associated with a plurality ofresources, a downgrade set of the plurality of resources; determinewhether a downgrade-only resource transformation scenario characterizedby downgrade transformation of the downgrade set of the plurality ofresources satisfies the one or more resource demand conditions; inresponse to determining that the downgrade-only resource transformationscenario satisfies the one or more resource demand conditions, generatean optimized resource transformation scenario based at least in part onthe downgrade-only resource transformation scenario; and in response todetermining that the downgrade-only resource transformation scenariofails to satisfy the one or more resource demand conditions: (i)identify one or more residual resources of the plurality of resourcesthat are deemed transformable to meet the one or more resource demandconditions but are not in the downgrade set, (ii) process the one ormore residual resources using a resource optimization machine learningmodel that is characterized by the set of resource optimizationparameters to generate one or more resource priority scores for the oneor more residual resources, (iii) generate a hybrid resourcetransformation scenario based at least in part on the one or moreresource priority scores and the downgrade-only resource transformationscenario, and (iv) generate the optimized resource transformationscenario based at least in part on the hybrid resource transformationscenario; and cause one or more resource transformation actions to beperformed based at least in part on the optimized resourcetransformation scenario.

In accordance with yet another aspect of the present disclosure, acomputer program product is provided. In particular embodiments, thecomputer program product includes a non-transitory computer storagemedium having instructions stored therein. The instructions beingconfigured to cause one or more computer processors to at least performoperations configured to: identify a demand surge scenario, wherein thedemand surge scenario is associated with one or more resource demandconditions; in response to identifying the demand surge scenario:determine, based at least in part on a plurality of resourcetransformation data objects associated with a plurality of resources, adowngrade set of the plurality of resources; determine whether adowngrade-only resource transformation scenario characterized bydowngrade transformation of the downgrade set of the plurality ofresources satisfies the one or more resource demand conditions; inresponse to determining that the downgrade-only resource transformationscenario satisfies the one or more resource demand conditions, generatean optimized resource transformation scenario based at least in part onthe downgrade-only resource transformation scenario; and in response todetermining that the downgrade-only resource transformation scenariofails to satisfy the one or more resource demand conditions: (i)identify one or more residual resources of the plurality of resourcesthat are deemed transformable to meet the one or more resource demandconditions but are not in the downgrade set, (ii) process the one ormore residual resources using a resource optimization machine learningmodel that is characterized by the set of resource optimizationparameters to generate one or more resource priority scores for the oneor more residual resources, (iii) generate a hybrid resourcetransformation scenario based at least in part on the one or moreresource priority scores and the downgrade-only resource transformationscenario, and (iv) generate the optimized resource transformationscenario based at least in part on the hybrid resource transformationscenario; and cause one or more resource transformation actions to beperformed based at least in part on the optimized resourcetransformation scenario.

In particular embodiments, each resource transformation data object fora resource of the plurality of resources that is either in the downgradeset or among the one or more residual resources indicates that theresource has a capability to be transformed to satisfy the one or moreresource demand conditions within an expected time occurrence for thedemand surge scenario. In addition, in particular embodiments,identifying the demand surge scenario involves: generating, using atrend surge prediction machine learning model, a trend surge indicatorbased at least in part on a recent trend for one or more resource demandindicators associated with the one or more resource demand conditions;generating, using a historical surge prediction machine learning model,a historical surge indicator based at least in part on a cyclical trendfor the one or more resource demand indicators; generating a surge scorebased at least in part on the trend surge indicator and the historicalsurge indicator; and identifying the demand surge scenario based atleast in part on whether the surge score satisfies a surge scorethreshold. In other embodiments, identifying the demand surge scenarioinvolves: identifying a first demand surge scenario associated with oneor more first resource demand conditions; identifying a second demandsurge scenario associated with one or more second resource demandconditions; determining the one or more resource demand conditions basedat least in part on the one or more first resource demand conditions andthe one or more second resource demand conditions; and determining thedemand surge scenario based at least in part on the one or more resourcedemand conditions.

In some embodiments, the one or more resource demand conditions arebased at least in part on merging the one or more first resource demandconditions and the one or more second resource demand conditions togenerate the one or more resource demand conditions. In otherembodiments, the one or more resource demand conditions are based atleast in part on a shared subset of the one or more first resourcedemand conditions and the one or more second resource demand conditions.Yet, in other embodiments, the one or more resource demand conditionsare based at least in part on: determining a first demand surge priorityscore for the first demand surge scenario; determining a second demandsurge priority score for the second demand surge scenario; in responseto determining that the first demand surge priority score exceeds thesecond demand surge priority score, determining the one or more resourcedemand conditions based at least in part on the one or more firstresource demand conditions; and in response to determining that thesecond demand surge priority score exceeds the first demand surgepriority score, determining the one or more resource demand conditionsbased at least in part on the one or more second resource demandconditions.

Furthermore, in some embodiments, each resource transformation dataobject for a resource of the plurality of resources that is in thedowngrade set describes that the resource can be transformed to satisfyat least one of the one or more resource demand conditions for at leastone of no cost or a cost satisfying a threshold cost. In addition, insome embodiments, the set of resource optimization parameters comprisesat least one of a transformation cost parameter or a transformation timeparameter. Finally, in some embodiments, the one or more resourcetransformation actions comprise automatically executing one or moreoperations to have one or more resources identified in the optimizedresource transformation scenario transformed to satisfy the one or moreresource demand conditions.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 is a diagram of a system architecture that can be used inconjunction with various embodiments of the present disclosure;

FIG. 2 is a schematic of a computing entity that may be used inconjunction with various embodiments of the present disclosure;

FIG. 3 is a data architecture that can be used in accordance withvarious embodiments of the present disclosure;

FIG. 4 is a process flow for identifying a demand surge scenario inaccordance with various embodiments of the present disclosure;

FIG. 5 is a process flow for addressing multiple demand surge scenariosin accordance with various embodiments of the present disclosure;

FIG. 6 is a process flow for identifying a rollback of a demand surgescenario in accordance with various embodiments of the presentdisclosure;

FIG. 7 is a process flow for dynamically allocating resources to satisfyone or more resource demand conditions in accordance with variousembodiments of the present disclosure;

FIG. 8 is a process flow for rolling back resources allocated to satisfyone or more resource demand conditions in accordance with variousembodiments of the present disclosure;

FIG. 9 provides an operational example of recent trend generation inaccordance with various embodiments of the present disclosure; and

FIG. 10 provides an operational example of historical trend generationin accordance with various embodiments of the present disclosure; and

FIG. 11 provides an operational example of a prediction output userinterface in accordance with various embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Various embodiments of the present disclosure now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the disclosure are shown. Indeed, thedisclosure may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” (also designated as “/”) is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative” and “exemplary” are used to beexamples with no indication of quality level. Like numbers refer to likeelements throughout.

Definitions of Certain Terms

The term “demand surge scenario” may refer to a data object thatdescribes one or more events leading to one or more resource demandconditions. Resource demand conditions may be conditions requiring oneor more resources to address a need deriving from the demand surgescenario. Oftentimes, events involved in demand surge scenarios areunforeseen by responsible person. For example, in the healthcareindustry, a demand surge scenario may represent the occurrence of anevent such as an epidemic, a medical emergency affecting multiplepersons such as a multiple car crash incident, or other event thatresults in a markedly increase in volume of patients who are in need ofsome type of medical service requiring one or more resources such asphysicians, nurses, specific medications, hospital beds, and/or thelike. In another example, a demand surge scenario may be seen in anindustry manufacturing a particular product when occurrence of an eventtakes place that results in a resource demand condition based at leastin part on consumer demand for the product markedly exceeding normaldemand. For instance, a demand surge scenario may be a natural disastersuch as a hurricane that may result in a resource demand condition basedat least in part on a markedly increase in demand for power generatorsrequiring the need of raw material resources to manufacturer the powergenerators. Thus, a demand surge scenario often results in one or moreresource demand conditions that need to be satisfied to adequatelyaddress the demand surge scenario.

The term “trend surge prediction machine learning model” may refer to adata object that describes parameters and/or hyper-parameters (e.g.,defined operations) of a model configured to predict a current trend forthe development of a demand surge scenario that may lead to one or moreresource demand conditions. For example, the demand surge scenario mayinvolve a surge in demand for a service and/or product that results inone or more resource demand conditions that need to be satisfied toaddress the demand surge scenario for the service and/or product. Invarious embodiments, the trend surge prediction machine learning modelmay be configured to generate a trend surge indicator representing thecurrent trend for the development of the demand surge scenario based atleast in part on a recent trend for one or more resource demandindicators associated with the one or more resource demand conditions.For example, for a healthcare facility, the one or more resource demandindicators may include patient intake data such as data describing thenumber of patients who are admitted into the healthcare facility,demographics of the patients, underlying medical conditions of thepatients, chronic medical conditions of the patients, severity of theconditions of the patients, date and time of intake of the patients,specialties required by the patients, and/or the like. Depending on theembodiment, the trend surge prediction machine learning model may beconfigured as any number of different types of models in predicting thecurrent trend in developing the demand surge scenario such as, forexample, a combination of one or more of exponential moving averagemodels, auto regression models, linear regression models, polynomialregression models, autoregressive-moving-average models, seasonalautoregressive-moving-average models,autoregressive-integrated-moving-average models, recurrent neuralnetwork models, and/or the like.

The term “historical surge prediction machine learning model” may referto a data object that describes parameters and/or hyper-parameters(e.g., defined operations) of a model configured to predict a historicaltrend for the development of a demand surge scenario that may lead toone or more resource demand conditions. In various embodiments, thehistorical surge prediction machine learning model may be configured togenerate a historical surge indicator representing the historical trendfor the development of the demand surge scenario based at least in parton a cyclical trend for one or more resource demand indicatorsassociated with the one or more resource demand conditions. Similar tothe trend surge prediction machine learning model, depending on theembodiment, the historical surge prediction machine learning model maybe configured as any number of different types of models in predictingthe historical trend for developing the demand surge scenario such as,for example, a combination of one or more of exponential moving averagemodels, auto regression models, linear regression models, polynomialregression models, autoregressive-moving-average models, seasonalautoregressive-moving-average models,autoregressive-integrated-moving-average models, recurrent neuralnetwork models, and/or the like.

The term “surge score” may refer to a data object representing a valuedetermined to represent a final measure of a trend in developing ademand surge scenario that takes into account both the trend surgeindicator and the historical surge indicator. For instance, inparticular embodiments, the surge score is generated as the trend surgeindicator plus the historical surge indicator, divided by the sum of thetwo. Accordingly, in various embodiments, the demand surge scenario maybe identified based at least in part on the surge score satisfying asurge threshold.

The term “resource transformation data object” may refer to a dataobject representing a resource used in addressing a resource demandcondition. For example, a resource may be some type of material ornonmaterial drawn upon to address a resource demand condition such as amanufacturing raw material, component, equipment, supply, personnel,facility, and/or the like. In various embodiments, the resourcetransformation data object may represent a resource that is required togo through some type of transformation to be able to be used to satisfythe resource demand condition. For example, the resource may be ahealthcare professional working for a healthcare facility who is trainedin treating respiratory disorders. In this example, to deploy thisprofessional to perform surgeries and/or treatment for disorders otherthan respiratory disorders, the professional may be required toundertake some type of transformation such as additional training.Accordingly, in various embodiments, one or more transformationparameters may be associated with a resource transformation data objectfor a resource that are related to transforming the resource to have thecapacity to satisfy a demand condition such as, for example,transformation cost, transformation time, and/or the like. In addition,in some embodiments, other types of data may be associated with aresource transformation data object for a resource such as, for example,what resource demand conditions the resource may address throughtransformation, and whether the resource requires a downgradetransformation or an upgrade transformation to meet those other resourcedemand conditions. In particular embodiments, a downgrade transformationinvolves transforming the resource to satisfy a resource demandcondition at no cost or at a cost satisfying (e.g., equal to or below) athreshold cost. While an upgrade transformation involves transformingthe resource to satisfy the resource demand condition at a cost or at acost not satisfying (e.g., above) the threshold cost.

The term “optimized resource transformation scenario” may refer to adata object providing a set of resource transformation data objectsrepresenting resources that can be transformed to meet one or moreresource demand conditions that are the result of a demand surgescenario. In particular embodiments, the set of resource transformationdata objects identified by the optimized resource transformationscenario may be optimized with respect to the use of the correspondingresources in addressing the one or more resource demand conditions.Accordingly, in these embodiments, such optimization may be carried outbased at least in part on whether a resource involves a downgrade orupgrade transformation, and/or a set resource optimization parametersfor resources that involve an upgrade transformation. In someembodiments, the optimized resource transformation scenario may provideadditional data such as, for example, data describing the type oftransformation required for a resource (e.g., downgrade or upgrade), thetime and/or cost associated with the transformation, an indication ofwhether the resource is going to be underused during the demand surgescenario, and/or the like. Accordingly, in various embodiments, theoptimized resource transformation scenario may be used in carrying outone or more resource transformation actions. These actions generallyinvolve transforming the associated resources so they may be used tosatisfy the one or more resource demand conditions. Depending on theembodiment, such actions may be carried out manually and/orautomatically.

The term “resource optimization machine learning model” may refer to adata object that describes parameters and/or hyper-parameters (e.g.,defined operations) of a model configured to optimize the use ofresources requiring an upgrade transformation to address one or moreresource demand conditions. For example, in some embodiments, theresource optimization machine learning model may be a supervised or anunsupervised machine learning model that is characterized by a set ofresource optimization parameters to generate one or more resourcepriority scores for one or more resources. In other embodiments, theresource optimization machine learning model is a rule-based model thatis characterized by a set of resource optimization parameters togenerate one or more resource priority scores for one or more resources.Here, depending on the embodiment, the set of resource optimizationparameters may involve, for example, parameters associated with a costof transforming the resource to address the one or more resource demandconditions, a time required to transform the resource to address the oneor more resource demand conditions, an opportunity cost associated withtransforming the resource to address the one or more resource demandconditions, and/or the like. Accordingly, the resource priority scorefor a resource may describe a priority designation for using theresource to address the one or more resource demand conditions withrespect to the other resources. In particular embodiments, a hybridresource transformation scenario may be generated based at least in parton the one or more resource priority scores and a downgrade-onlyresource transformation scenario identifying resources that involve adowngrade transformation to satisfy the one or more resource demandconditions. In some embodiments, the hybrid resource transformationscenario may be used in generating the optimized resource transformationscenario.

The term “demand surge priority score” may refer to a data objectrepresenting an importance of satisfying the one or more resource demandconditions resulting from a demand surge scenario with respect to otherdemand surge scenarios that may be occurring at virtually the same time.Accordingly, in various embodiments, the demand surge priority score maybe used in prioritizing simultaneously occurring demand surge scenariosso that the resource demand conditions associated with the scenario(s)of higher importance are satisfied first.

Overview

Embodiments of the disclosure provide a novel approach for addressing ademand surge scenario involving one or more resource demand conditionsby systematically and proactively recommending resources that can betransformed to satisfy the resource demand conditions. Here, a resourcemay refer to some type of material or nonmaterial drawn upon to satisfya resource demand condition resulting from the demand surge scenariosuch as, for example, a manufacturing raw material, a product, a good, acomponent, a commodity, equipment, a supply, personnel, a facility,and/or the like. Accordingly, various embodiments of the disclosureinvolve producing recommendations for resources that can be transformedfor use to satisfy one or more demand surge conditions resulting from ademand surge scenario by balancing levels of effort, costs, and/or thelike involved in transforming the resources better than can otherwise beperformed conventionally through both manual and automated processes.

As described further herein, various embodiments of the frameworkinvolve three main components. The first of these components is aresource repository for storing data on resources that may be used insatisfying a resource demand condition. Specifically, in particularembodiments, the resource repository contains data on specific resourcesavailable to satisfy resource demand conditions and how such resourcescan be transformed to address the resource demand conditions.Accordingly, the resource repository is used in embodiments of theframework in identifying resources having the capability to betransformed to satisfy resource demand conditions, along with the timeand cost needed to transform these resources to be used to satisfy theresource demand conditions.

The second of these components of the framework is a process for surgedemand detection. Accordingly, in various embodiments, the surge demanddetection process is configured to detect demand surge scenarios in realtime and whether the scenarios satisfy a threshold indicating theyshould be addressed. Here, in particular embodiments, the surge demanddetection process tracks various trend surge indicators that can be usedin identifying a demand surge scenario resulting in one or more resourcedemand conditions. As a result of identifying a demand surge scenariothat should be addressed, the surge demand detection process continuesby triggering a dynamic allocation process to then generaterecommendations on resources that can be transformed to address theresource demand conditions resulting from the demand surge scenario.

Thus, the third component of the framework is the dynamic allocationprocess. Accordingly, in various embodiments, the dynamic allocationprocess receives a request for specific resources to satisfy one or moreresource demand conditions resulting from a demand surge scenario, andgenerates an optimized resource transformation scenario based at leastin part on the availability, a set of resource optimization parameters,and capabilities of various resources that can be transformed to satisfythe resource demand conditions. Further detail is provided herein on thevarious components of the framework.

Exemplary Technical Contributions

Many major medical incidents can lead to what is referred to as amedical demand surge scenario. Such incidents include, for example, sometype of mass outbreak of disease such as an epidemic or pandemic or anevent that results in a high number of persons needing medical attentionsuch as a disaster, large-scale accident, terrorist attack, and/or thelike. A healthcare service facility's system resiliency is viewed as thefacility's ability to survive such a medical demand surge scenario andmaintain or rapidly recover operations that have been compromised due tothe medical demand surge scenario. Accordingly, the healthcare servicefacility's medical demand surge capacity refers to the facility'sability to evaluate and care for a markedly increased volume ofpatients, one that challenges or exceeds normal operating capacity.

Conventional initiatives to address medical demand surge capacitytypically focus on identifying adequate numbers of needed resources toaddress the surge scenario such as, for example, hospital beds, medicalpersonnel, medications, supplies, and equipment. The problem with thisapproach is that many of these initiatives rely on having necessarystandby quantities of each of these resources to address medical demandsurge scenarios. In addition, many of these initiatives require adequateand timely recognition of when a medical demand surge scenario is likelyto occur so that resource needs can be identified and movedexpeditiously to locations where required to address resource demandconditions resulting from the medical demand surge scenario.

One approach to address some of the disadvantages associated withmanaging and maintaining standby quantities of resources is havingsystems and operations in place that can maximize the use and abilitiesof existing resources found within a healthcare service facility,resulting in a reduced need for standby resources. However, conventionalsystems and operations found within many healthcare service facilitiesdo not have the capabilities to maximize the abilities of existingresources, leading to many technical challenges in using theseconventional systems and operations for addressing a medical demandsurge scenario. As a result, many healthcare service facilities arereduced to having to manually review and analyze the resources availableso that they may be adequately deployed to satisfy resource demandconditions resulting from the medical demand surge scenario.

However, management of medical demand surge scenarios and the resourcesneeded to satisfy resource demand conditions resulting from such surgescenarios is oftentimes beyond the capabilities of humans. Firstly,humans do not normally have the capability to recognize when a medicaldemand surge scenario is likely to occur. Some medical demand surgescenarios may be recognizable by a human because they are easilyvisible. For example, an event that has occurred, such as a naturaldisaster, resulting in a high number of persons needing medicalattention may be recognizable by medical personnel at a facilityexpected to receive such persons. However, many medical demand surgescenarios, such as an epidemic, may begin to develop over a period oftime that is not easily recognizable (e.g., visible) to a human (e.g., afew weeks) until they result in an exponential volume of patients in ashort period of time. As a result, a medical service facility may befaced with an unexpected/unforeseen medical surge demand scenario thatthe facility has not adequately prepared the capabilities and resourcesto handle.

Secondly, humans do not normally have the capability to manage existingresources available to a healthcare service facility so that they can beused efficiently and effectively to address a medical demand surgescenario. Such management requires maintaining an accurate inventory ofthe resources available to the healthcare service facility, as well asthe analytical capabilities to identify and deploy available resourcesin an effective, efficient, timely manner to address a medical demandsurge scenario. This is especially true with respect to availableresources that may need to be transformed to a different use anddeployed to address a particular resource demand condition resultingfrom a medical demand surge scenario. Thus, management of medical demandsurge scenarios and the resources needed to meet such surge scenarios isnormally beyond the capabilities of the human mind. As a result,healthcare service facilities that employ manual management of medicaldemand surge scenarios run the risk of many dire consequences that canresult from a medical demand surge scenario such as inadequate medicalcare of patients, unnecessary spread of infectious diseases, and loss ofhuman life.

Accordingly, various embodiments of the disclosure provided hereinaddress many of the technical disadvantages encountered in conventionalsystems and processes used in recognizing and addressing medical demandsurge scenarios that result in one or more resource demand conditionsthat need to be satisfied to address the medical demand surge scenarios.Specifically, embodiments of the disclosure provide a novel approachthat systematically and proactively recommend transformation ofresources to meet resource demand conditions for specific resourcedemand surge scenarios. As a result, embodiments of the disclosureovercome many of the technical disadvantages of conventional systems andprocesses used in managing medial demand surge scenarios by providingcapabilities that go beyond those seen in such conventional systems andprocesses.

In addition, various embodiments of the disclosure allow for managementand deployment of resources to satisfy resource demand conditionsresulting from resource demand scenarios that is normally handled byhumans to be carried out in an automated fashion without humanintervention. Here, embodiments facilitate the automatic identificationand transformation of resources to satisfy resource demand conditionsresulting from demand surge scenarios, as well as the automaticexecution of actions to transform such resources. Thus, the disclosedsolution is more effective, accurate, less error prone, and faster thanmanual implementations. In addition, various embodiments'implementations reduce the manual effort necessary to address resourcedemand conditions resulting from demand surge scenarios and reduceoperational costs and inefficiencies.

Further, the systematic and proactive transformation of resourcesexecuted in various embodiments to satisfy resource demand conditionsresulting from demand surge scenarios can carry out complex mathematicaloperations that cannot be performed by the human mind. Additionally, thesolution can reduce the computational load of various systems used inperforming tasks by using the recommendations for transforming resourcesto satisfy resource demand conditions while marginally affecting theeffective throughput of these systems. Accordingly, various embodimentsof the present disclosure enhance the efficiency and speed of variouscomputing systems by providing the ability to computationally manage avery large number of resources used in satisfying resource demandconditions resulting from demand surge scenarios in an efficient manner,and make important contributions to the various computational tasks thatutilize real-time/expedited processing to recognize demand surgescenarios and generate recommendations for transforming resources tosatisfy resource demand conditions resulting from these demand surgescenarios. In doing so, various embodiments of the present disclosuremake major technical contributions to improving the computationalefficiency and reliability of various automated systems and proceduresfor carrying out these tasks. This, in turn, translates to morecomputationally efficient software systems.

Moreover, various embodiments of the present invention increase thecomputational efficiency of performing optimized resource transformationby generating hybrid resource transformation scenarios only in responseto determining that a downgrade-only resource transformation scenariofails to satisfy one or more resource demand conditions. This in turnavoids the substantial computational costs of performing resourcetransformation optimizations associated with generating hybrid resourcetransformation scenarios when a downgrade-only resource transformationscenario fails to satisfy one or more resource demand conditions, and indoing so in some embodiments reduces the number of processing operationsthat need to be performed in order to perform optimized resourcetransformation. In this way, the noted embodiments of the presentinvention reduce the number of processing cycles that need to beperformed in order to perform optimized resource transformation, whichin turn improves the computational cost of performing optimized resourcetransformation and makes substantial improvements to various sub-fieldsof the fields of optimized resource transformation and predictive dataanalysis.

Computer Program Products, Systems, Methods, and Computing Entities

Embodiments of the present disclosure may be implemented in variousways, including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, and/or the like. A software component may be coded inany of a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present disclosure may take the form of a data structure, apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present disclosuremay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisesa combination of computer program products and hardware performingcertain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially, such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel, such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

Exemplary System Architectures

FIG. 1 provides an illustration of a system architecture 100 that may beused in accordance with various embodiments of the disclosure. Here, thearchitecture 100 includes various components involved in identifyingdemand surge scenarios, generating optimized resource transformationscenarios for such demand surge scenarios, and/or executing one or moreactions to have resources identified in the optimized resourcetransformation scenarios transformed so that such resources can satisfyresource demand conditions resulting from the demand surge scenarios.Accordingly, the components may include one or more application servers110 that may be in communication with and one or more data sources 115,120, 125 over one or more networks 130. It should be understood that theapplication server(s) 110 may be made up of several servers, storagemedia, layers, and/or other components, which may be chained orotherwise configured to interact and/or perform tasks. Specifically, theapplication server(s) 110 may include any appropriate hardware and/orsoftware for interacting with the data sources 115, 120, 125 as neededto execute aspects of one or more applications for processing dataacquired from the data sources 115, 120, 125 and handling data accessand business logic for such.

In addition, the architecture 100 may include one or more computingdevices 135 used by individuals for conducting one or more processesthat make use of optimized resource transformation scenarios generatedfor identified demand surge scenarios. For example, the computingdevices 135 may be used by administrators at a healthcare servicefacility in conducting an analysis on an optimized resourcetransformation scenario generated for a demand surge scenario toidentify which available resources should be transformed to satisfy oneor more resource demand conditions resulting from the demand surgescenario, and to initiate one or more transformation actionsaccordingly. Here, the device(s) 135 may be one of many different typesof devices such as, for example, a desktop or laptop computer or amobile device such as a smart phone or tablet.

As noted, the application server(s) 110, data sources 115, 120, 125, andcomputing device(s) 135 may communicate with one another over one ormore networks 130. Depending on the embodiment, these networks 130 maycomprise any type of known network such as a land area network (LAN),wireless land area network (WLAN), wide area network (WAN), metropolitanarea network (MAN), wireless communication network, the Internet, etc.,or combination thereof. In addition, these networks 130 may comprise anycombination of standard communication technologies and protocols. Forexample, communications may be carried over the networks 130 by linktechnologies such as Ethernet, 802.11, CDMA, 3G, 4G, or digitalsubscriber line (DSL). Further, the networks 130 may support a pluralityof networking protocols, including the hypertext transfer protocol(HTTP), the transmission control protocol/internet protocol (TCP/IP), orthe file transfer protocol (FTP), and the data transferred over thenetworks 130 may be encrypted using technologies such as, for example,transport layer security (TLS), secure sockets layer (SSL), and internetprotocol security (IPsec). Those skilled in the art will recognize FIG.1 represents but one possible configuration of a system architecture100, and that variations are possible with respect to the protocols,facilities, components, technologies, and equipment used.

Exemplary Computing Entity

FIG. 2 provides a schematic of a computing entity 200 that may be usedin accordance with various embodiments of the present disclosure. Forinstance, the computing entity 200 may be one or more of the applicationservers 110, and in some instances one or more of the computing devices135, previously described in FIG. 1 . In general, the terms computingentity, entity, device, system, and/or similar words used hereininterchangeably may refer to, for example, one or more computers,computing entities, desktop computers, mobile phones, tablets, phablets,notebooks, laptops, distributed systems, items/devices, terminals,servers or server networks, blades, gateways, switches, processingdevices, processing entities, set-top boxes, relays, routers, networkaccess points, base stations, the like, and/or any combination ofdevices or entities adapted to perform the functions, operations, and/orprocesses described herein. Such functions, operations, and/or processesmay include, for example, transmitting, receiving, operating on,processing, displaying, storing, determining, creating/generating,monitoring, evaluating, comparing, and/or similar terms used hereininterchangeably. In one embodiment, these functions, operations, and/orprocesses can be performed on data, content, information, and/or similarterms used herein interchangeably.

Although illustrated as a single computing entity, those of ordinaryskill in the art should appreciate that the computing entity 200 shownin FIG. 2 may be embodied as a plurality of computing entities, tools,and/or the like operating collectively to perform one or more processes,methods, and/or steps. As just one non-limiting example, the computingentity 200 may comprise a plurality of individual data tools, each ofwhich may perform specified tasks and/or processes.

Depending on the embodiment, the computing entity 200 may include one ormore network and/or communications interfaces 225 for communicating withvarious computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. Thus, in certain embodiments, the computing entity 200may be configured to receive data from one or more data sources and/ordevices as well as receive data indicative of input, for example, from adevice.

The networks used for communicating may include, but are not limited to,any one or a combination of different types of suitable communicationsnetworks such as, for example, cable networks, public networks (e.g.,the Internet), private networks (e.g., frame-relay networks), wirelessnetworks, cellular networks, telephone networks (e.g., a public switchedtelephone network), or any other suitable private and/or publicnetworks. Further, the networks may have any suitable communicationrange associated therewith and may include, for example, global networks(e.g., the Internet), MANs, WANs, LANs, or PANs. In addition, thenetworks may include any type of medium over which network traffic maybe carried including, but not limited to, coaxial cable, twisted-pairwire, optical fiber, a hybrid fiber coaxial (HFC) medium, microwaveterrestrial transceivers, radio frequency communication mediums,satellite communication mediums, or any combination thereof, as well asa variety of network devices and computing platforms provided by networkproviders or other entities.

Accordingly, such communication may be executed using a wired datatransmission protocol, such as fiber distributed data interface (FDDI),digital subscriber line (DSL), Ethernet, asynchronous transfer mode(ATM), frame relay, data over cable service interface specification(DOCSIS), or any other wired transmission protocol. Similarly, thecomputing entity 200 may be configured to communicate via wirelessexternal communication networks using any of a variety of protocols,such as general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Global System for Mobile Communications (GSM), Enhanced Datarates for GSM Evolution (EDGE), Time Division-Synchronous Code DivisionMultiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol. The computingentity 200 may use such protocols and standards to communicate usingBorder Gateway Protocol (BGP), Dynamic Host Configuration Protocol(DHCP), Domain Name System (DNS), File Transfer Protocol (FTP),Hypertext Transfer Protocol (HTTP), HTTP over TLS/SSL/Secure, InternetMessage Access Protocol (IMAP), Network Time Protocol (NTP), Simple MailTransfer Protocol (SMTP), Telnet, Transport Layer Security (TLS), SecureSockets Layer (SSL), Internet Protocol (IP), Transmission ControlProtocol (TCP), User Datagram Protocol (UDP), Datagram CongestionControl Protocol (DCCP), Stream Control Transmission Protocol (SCTP),HyperText Markup Language (HTML), and/or the like.

In addition, in various embodiments, the computing entity 200 includesor is in communication with one or more processing elements 210 (alsoreferred to as processors, processing circuitry, and/or similar termsused herein interchangeably) that communicate with other elements withinthe computing entity 200 via a bus 230, for example, or networkconnection. As will be understood, the processing element 210 may beembodied in several different ways. For example, the processing element210 may be embodied as one or more complex programmable logic devices(CPLDs), microprocessors, multi-core processors, coprocessing entities,application-specific instruction-set processors (ASIPs), and/orcontrollers. Further, the processing element 210 may be embodied as oneor more other processing devices or circuitry. The term circuitry mayrefer to an entirely hardware embodiment or a combination of hardwareand computer program products. Thus, the processing element 210 may beembodied as integrated circuits, application specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), programmablelogic arrays (PLAs), hardware accelerators, other circuitry, and/or thelike. As will therefore be understood, the processing element 210 may beconfigured for a particular use or configured to execute instructionsstored in volatile or non-volatile media or otherwise accessible to theprocessing element 210. As such, whether configured by hardware,computer program products, or a combination thereof, the processingelement 210 may be capable of performing steps or operations accordingto embodiments of the present disclosure when configured accordingly.

In various embodiments, the computing entity 200 may include or be incommunication with non-volatile media (also referred to as non-volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). For instance, the non-volatile storage ormemory may include one or more non-volatile storage or memory media 220,such as hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SDmemory cards, Memory Sticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrackmemory, and/or the like. As will be recognized, the non-volatile storageor memory media 220 may store files, databases, database instances,database management system entities, images, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like. The term database, database instance, databasemanagement system entity, and/or similar terms used hereininterchangeably and, in a general sense, to refer to a structured orunstructured collection of information/data that is stored in acomputer-readable storage medium.

In particular embodiments, the memory media 220 may also be embodied asa data storage device or devices, as a separate database server orservers, or as a combination of data storage devices and separatedatabase servers. Further, in some embodiments, the memory media 220 maybe embodied as a distributed repository such that some of the storedinformation/data is stored centrally in a location within the system andother information/data is stored in one or more remote locations.Alternatively, in some embodiments, the distributed repository may bedistributed over a plurality of remote storage locations only. Asalready discussed, various embodiments contemplated herein communicatewith various information sources and/or devices in which some or all theinformation/data required for various embodiments of the disclosure maybe stored.

In various embodiments, the computing entity 200 may further include orbe in communication with volatile media (also referred to as volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). For instance, the volatile storage ormemory may also include one or more volatile storage or memory media 215as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cachememory, register memory, and/or the like. As will be recognized, thevolatile storage or memory media 215 may be used to store at leastportions of the databases, database instances, database managementsystem entities, data, images, applications, programs, program modules,scripts, source code, object code, byte code, compiled code, interpretedcode, machine code, executable instructions, and/or the like beingexecuted by, for example, the processing element 210. Thus, thedatabases, database instances, database management system entities,data, images, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like may be used to controlcertain aspects of the operation of the computing entity 200 with theassistance of the processing element 210 and operating system.

As will be appreciated, one or more of the computing entity's componentsmay be located remotely from other computing entity components, such asin a distributed system. Furthermore, one or more of the components maybe aggregated and additional components performing functions describedherein may be included in the computing entity 200. Thus, the computingentity 200 can be adapted to accommodate a variety of needs andcircumstances.

Exemplary System Operations

The logical operations described herein may be implemented (1) as asequence of computer implemented acts or one or more program modulesrunning on a computing system and/or (2) as interconnected machine logiccircuits or circuit modules within the computing system. Theimplementation is a matter of choice dependent on the performance andother requirements of the computing system. Accordingly, the logicaloperations described herein are referred to variously as states,operations, structural devices, acts, or modules. These states,operations, structural devices, acts, and modules may be implemented insoftware, in firmware, in special purpose digital logic, and anycombination thereof. Greater or fewer operations may be performed thanshown in the figures and described herein. These operations may also beperformed in a different order than those described herein.

Exemplary Data Architecture

FIG. 3 provides a data architecture 300 for storing data on resourcesavailable to a facility in accordance with various embodiments of thedisclosure. For instance, this particular architecture 300 may beutilized as a resource repository provided via one or more filestructures and/or one or more database structures in particularembodiments. Accordingly, the primary structures 310, 320, 330, 340, 350shown in FIG. 3 may be constructed as individual files or tablesdepending on whether a file structure or a database structure is used.

Looking at FIG. 3 , the architecture 300 includes a Facility Resourcesstructure 310 configured to store data on various resources that areavailable to a facility. For instance, the facility may be a healthcareservice facility (e.g., hospital) and the resources available to thefacility may include medical personnel, medical equipment, medications,various types of facilities such as ICU rooms and operating rooms,and/or the like. For this particular configuration, the FacilityResources structure 310 includes a resource identifier (Resource ID 311)for each available resource that uniquely identifies the resource.Accordingly, this identifier can be used to retrieve, as well asassociate, data stored in other data structures for the resource. TheFacility Resources structure 310 may also include a description of theresource (Resource Description 312) that provides a general descriptionof the resources such as, for example, ICU room. In addition, theFacility Resources structure 310 includes a resource type (Resource Type313) that identifies a type for the resource such as, for example,employee, medical equipment, medical supply, facility structure, and/orthe like. Depending on the embodiment, the Facility Resources structure310 may include other data and/or the data may be separated out intomultiple data elements (e.g., fields) and/or multiple structures forstorage purposes.

In addition, the architecture 300 in this particular instance includes aSpecialties structure 330 configured to store data on various medicalspecialties provided by healthcare service facility. For example, amedical specialty provided by the healthcare service facility mayinclude outpatient surgeries, emergency room services, ICU services,chemotherapy treatments, and/or the like. Similar to the FacilityResources structure 310, the Specialties structure 330 includes anidentifier (Specialty ID 331) and a description (Specialty Description332) for each of the specialties provided by the healthcare servicefacility. In various embodiments, the use of these specialties aremonitored to identify demand surge scenarios involving the specialties.For example, a demand surge scenario may involve an increased demand forICU services due to the outbreak of an epidemic. Accordingly, one ormore resource demand conditions can result from the occurrence of ademand surge scenario. For example, a demand surge scenario involvingthe increased demand for ICU services may result in resource demandconditions related to requirements for isolation spaces (e.g., roomsand/or beds) and the need for additional medical staff to assist in theICU unit.

Thus, the data architecture 300 in this particular instance alsoincludes a Resource Demand Conditions structure 320 configured to storedata for the different resource demand conditions that may result from ademand surge scenario. The Resource Demand Conditions structure 320includes an identifier (Condition ID 321) for each resource demandcondition that may occur that uniquely identifies the condition and canbe used to retrieve, as well as associate, data stored in other datastructures for the condition. In addition, the Resource DemandConditions structure 320 includes a data element (Condition Description322) that provides a general description of the resource demandcondition. Further, the Resource Demand Conditions structure 320includes a specialty identifier (Specialty ID 331) for the specialtyassociated with the resource demand condition. Therefore, returning tothe example involving the specialty of ICU services, the Resource DemandConditions structure 320 may include a data entry (e.g., a record) forthe resource demand condition resulting from a demand surge scenarioinvolving increased demand for ICU services that is related to therequirement for isolation spaces. Accordingly, this data entry mayinclude the specialty identifier (Specialty ID 331) for ICU services toindicate this particular resource demand condition is related to ademand surge scenario involving this specialty.

Continuing on, the data architecture 300 shown in FIG. 3 includes aResource to Resource Demand Condition structure 340 that is configuredto store data on the resources that can be used and/or transformed tosatisfy one or more resource demand conditions. Accordingly, theResource to Resource Demand Condition structure 340 includes theresource identifier (Resource ID 310) and the resource demand conditionidentifier (Condition ID 321). Here, the combination of these two dataelements for a particular data entry indicates the resource associatedwith the resource identifier for the entry can be used to satisfy theresource demand condition associated with the resource demand conditionidentifier for the entry.

For instance, returning to the example involving a demand surge scenarioinvolving an increase in demand for ICU services at a healthcare servicefacility due to an epidemic resulting in a resource demand condition forisolation space, a data entry may be found in the Resource to ResourceDemand Condition structure 340 for this resource demand condition withrespect to a resource available to the healthcare service facility thatis a room in the facility currently being used for office space. Here,this data entry may have a combination of the resource identifier forthe room being used as office space and the resource demand conditionidentifier for the resource demand condition for isolation space.Accordingly, this combination may identify that the room being used asoffice space can be used to satisfy the need for isolation space.

In this example, the room currently being used as office space may needto be transformed so that it may then be used as isolation space forpatients. For example, the office equipment currently in the room mayneed to be removed, and beds and monitoring equipment for patients mayneed to be installed in the room so that it may be used as isolationspace. Therefore, the Resource to Resource Demand Condition structure340 may include one or more data elements storing data on transformingthe resource so that the resource may satisfy the corresponding resourcedemand condition. For instance, the Resource to Resource DemandCondition structure 340 may include a data element (Operation 341)identifying whether the transformation of the resource involves adowngrade or an upgrade.

As discussed further herein, a downgrade transformation in variousembodiments may involve a transformation in which the resource can betransformed to satisfy a resource demand condition at no cost or at acost that satisfies a cost threshold. For example, the cost thresholdmay be set at $15,000, and therefore in this example, a transformationmay be identified as a downgrade if the cost of transforming theresource to satisfy the resource demand condition is $15,000 or less.Conversely, in this example, the transformation may be identified as anupgrade if the cost is over $15,000. Other factors may be considered insome embodiments in identifying a transformation as either a downgradeor an upgrade. For example, the time required to transform the resourceso that the resource may be used to satisfy the resource demandcondition may be considered in identifying the transformation for theresource as either a downgrade or an upgrade. Those of ordinary skill inthe art can envision other factors, or combinations thereof, that may beused in identifying a transformation of a resource as either a downgradeor an upgrade in light of this disclosure. Therefore, in variousembodiments, the Resource to Resource Demand Condition structure 340 mayinclude one or more data elements for storing data associated with thetransformation of the resource, such as cost (Cost 342), time (Time343), and/or the like.

Finally, the data architecture 300 shown in FIG. 3 includes a SpecialtyUse structure 350 configured for storing data on various resource demandindicators that may be used in identifying demand surge scenarios. Inthis instance, the Specialty Use structure 350 shown in FIG. 3 isconfigured based at least in part on the example involving thehealthcare service facility. Here, the healthcare service facility maybe interested in identifying demand surge scenarios that involve amarkedly increase intake of patients that may result in a demand surgescenario for one or more specialties being offered by the healthcareservice facility. Therefore, the Specialty Use structure 350 may includethe specialty identifier (Specialty ID 331) to identify the specialtyassociated with the resource demand indicators (e.g., a data entry ofresource demand indicators). Each data entry in the Specialty Usestructure 350 may involve a specific instance of a patient's use/need ofthe specialty at the healthcare service facility. For example, the dataused to populate the Specialty Use structure 350 may be acquired fromone or more intake of patient systems used within the healthcare servicefacility. Thus, the resource demand indicators may include data such as,for example, demographics on the patient (Patient Demographics 351),underlying medical conditions experienced by the patient (UnderlyingConditions 352), chronic medical conditions experienced by the patient(Chronic Conditions 353), date (Date 354) and time (Time 355) of thepatient's use/need of the specialty, severity of the condition (Severityof Condition 356) leading to the use/need of the specialty, and/or thelike. Accordingly, the Specialty Use structure 350 may include otherdata and/or the data may be separated out into multiple data elements(e.g., fields) and/or multiple structures for storage purposes.

Demand Surge Module

Turning now to FIG. 4 , additional details are provided regarding aprocess flow for identifying a demand surge scenario according tovarious embodiments. FIG. 4 is a flow diagram showing a demand surgemodule for performing such functionality according to variousembodiments of the disclosure. For example, the flow diagram shown inFIG. 4 may correspond to operations carried out by a processing element210 in a computing entity 200, such as an application server 110described in FIG. 1 , as it executes the demand surge module stored inthe computing entity's volatile and/or nonvolatile memory.

Therefore, the process flow 400 begins in various embodiments with thedemand surge module querying one or more resource demand indicators fromthe resource repository in Operation 410. Accordingly, in particularembodiments, the resource demand indicators may be associated with aparticular tangible or nontangible item such as a product or service(e.g., a specialty provided by a healthcare service facility) in whichan entity is interested in determining whether a demand surge scenariomay be occurring related to the tangible or nontangible item. Here, thedemand surge module may be configured to query the resource demandindicators for a specific window of time such as, for example, for amonth. Therefore, the queried resource demand indicators are associatedwith data collected for the indicators over the specific window of time.

The demand surge module then generates one or more trends using the oneor more resource demand indicators in Operation 415. Specifically, invarious embodiments, the demand surge module is configured to use atrend surge prediction machine learning model configured to generate atrend surge indicator representing the current trend for the developmentof a demand surge scenario based at least in part on a recent trend forthe one or more resource demand indicators. Depending on the embodiment,the trend surge prediction machine learning model may be configured asany number of different types of models in predicting the current trendin developing the demand surge scenario such as, for example, acombination of one or more of exponential moving average models, autoregression models, linear regression models, polynomial regressionmodels, autoregressive-moving-average models, seasonalautoregressive-moving-average models,autoregressive-integrated-moving-average models, recurrent neuralnetwork models, and/or the like.

Accordingly, the one or more resource demand indicators may be processedusing the trend surge prediction machine learning model to generate aprediction as to whether a recent trend has developed with respect toone or more resource demand conditions associated with the one or moreresource demand indicators. For example, the one or more resourceddemand indicators may be based at least in part on data gathered on theintake of patients at a healthcare service facility with respect to aspecialty offered by the facility. Therefore, the prediction generatedby the trend surge prediction machine learning model may indicatewhether the healthcare service facility is experiencing a currentincrease in demand for the specialty, leading to one or more resourcedemand conditions. In particular embodiments, the demand surge modulemay be further configured to generate a trend surge indicator based atleast in part on the prediction. For instance, in some embodiments, thedemand surge module may generate the trend surge indicator as thepercentage of inclination in the slope of the recent trend prediction.Here, the demand surge module may transform the degrees of the slope toa value between zero and one.

Although the trend surge indicator may measure the recent trend withrespect to the one or more resource demand conditions, the trend surgeindicator may be affected by any seasonality factors. For example, thepatient intake for a particular specialty at a healthcare servicefacility may be affected by seasonality factors such as flu season,college holidays, national holidays (e.g., Easter), and/or the like.Therefore, to balance the trend surge indicator with a better view ofseasonality trends in the past, the trend surge module is configured invarious embodiments to generate a historical surge indicator. Anoperational example of a recent trend generation is depicted in FIG. 9 .

Accordingly, in particular embodiments, the trend surge module generatesthe historical surge indicator by using a historical surge predictionmachine learning model configured to generate a prediction on ahistorical trend for the development of the demand surge scenario basedat least in part on a cyclical trend for the one or more resource demandindicators associated with the one or more resource demand conditions.Similar to the trend surge prediction machine learning model, dependingon the embodiment, the historical surge prediction machine learningmodel may be configured as any number of different types of models inpredicting the historical trend for developing the demand surge scenariosuch as, for example, a combination of one or more of exponential movingaverage models, auto regression models, linear regression models,polynomial regression models, autoregressive-moving-average models,seasonal autoregressive-moving-average models,autoregressive-integrated-moving-average models, recurrent neuralnetwork models, and/or the like. Thus, in particular embodiments, thedemand surge module may be further configured to generate the historicalsurge indicator based at least in part on the historical trendprediction. For instance, in some embodiments, the demand surge modulemay generate the historical surge indicator as the difference in theslope of the recent trend prediction and the historical trendprediction. Again, the demand surge module may transform the differencein the slope to a value between zero and one. An operational example ofa historical trend generation is depicted in FIG. 10 .

At this point, in various embodiments, the trend surge module generatesa surge score by combining the trend surge indicator and the historicalsurge indicator in Operation 420. For example, in particularembodiments, the trend surge module generates the surge score as thetrend surge indicator plus the historical surge indicator, divided bytwo. Depending on the embodiment, the trend surge module may beconfigured to use any one of different computations in combining the twoindicators to generate the surge score based at least in part on thetypes of models and measures used for the current trend prediction andhistorical trend prediction. Accordingly, the main focus of the surgescore in various embodiments is to balance the trend surge predictionagainst the historical surge prediction.

The trend surge module then determines whether the surge score satisfiesa surge threshold in Operation 425. Here, in particular embodiments, thevalue of the surge threshold may be determined based at least in part onthe knowledge of subject matter experts or the temporality of the surgescenarios and the facility being monitored. In some embodiments, thesurge threshold may be adapted using a machine learning model configuredto identify the optimal value for the threshold.

Accordingly, if the trend surge module determines the surge scoresatisfies the surge threshold, then the trend surge module identifies anoccurrence of a demand surge scenario in Operation 430. Here, the demandsurge scenario may be associated with one or more resource demandconditions that are a result of the demand surge scenario. For instance,returning to the example of the healthcare service facility evaluatingwhether a surge in demand for ICU service may be occurring, if such ademand surge scenario is identified to be occurring, then the resourcedemand conditions resulting from the demand surge scenario may includeincreased need of isolation space for patients and increased need ofmedical personnel to assist in the ICU unit at the facility. Asdiscussed further herein, at this point, various embodiments of thedisclosure involve identifying resources that can be transformed tosatisfy the one or more resource demand conditions resulting from thedemand surge scenario.

It is noted that in some embodiments, the trend surge module may beexecuted in parallel across the different tangible or nontangible items(e.g., across the different specialties offered by the healthcareservice facility) and/or resource demand indicators to evaluate each ofthe different tangible or nontangible items. Such an approach may becarried out to ensure detection of different demand surge scenarios thatmay be overlapping and/or producing similar resource demand conditions.As detailed further herein, depending on the embodiment, multiple demandsurge scenarios occurring at virtually the same time may be handled byidentifying the resources that can be transformed to satisfy theresource demand conditions for each demand surge scenario independentlyor by merging the resource demand conditions for the multiple demandsurge resources and then identifying the resources that can betransformed to satisfy the combined resource demand conditions.

Multiple Demand Surge Module

Turning now to FIG. 5 , additional details are provided regarding aprocess flow for addressing multiple demand surge scenarios according tovarious embodiments. FIG. 5 is a flow diagram showing a multiple demandsurge module for performing such functionality according to variousembodiments of the disclosure. For example, the flow diagram shown inFIG. 5 may correspond to operations carried out by a processing element210 in a computing entity 200, such as an application server 110described in FIG. 1 , as it executes the multiple demand surge modulestored in the computing entity's volatile and/or nonvolatile memory.

Here, in some embodiments, the multiple demand surge module may beinvoked in response to identifying a demand surge scenario. For example,the demand surge module previously discussed may be configured to invokethe multiple demand surge module upon identifying the occurrence of ademand surge scenario.

Therefore, the process flow 500 begins in various embodiments with themultiple demand surge module determining whether multiple demand surgescenarios are occurring at virtually the same time in Operation 510.Depending on the circumstances, the multiple demand surge scenarios mayhave developed simultaneously and may be overlapping, with a firstdemand surge scenario developing initially and a second demand surgescenario developing at a later time. For example, an epidemic may causesimultaneous occurrences of demand surge scenarios for a healthcareservice facility. Specifically, the epidemic may cause a first demandsurge scenario with respect to ICU services offered by the healthcareservice facility and a second demand surge scenario with respect tolaboratory testing services offered by the healthcare service facility.While in another example, the epidemic may cause a first demand surgescenario with respect to ICU services offered by the healthcare servicefacility to initially occur and the flu season may cause a second demandsurge scenario with respect to the ICU services offered by thehealthcare service facility to occur after the first demand surgescenario has begun.

If the multiple demand surge module determines that multiple demandsurge scenarios are not occurring, then the multiple demand surge modulesimply exits. Here, in various embodiments, an optimized resourcetransformation scenario may then be generated for the demand surgescenario that has been identified. However, if the multiple demand surgemodule determines that multiple demand surge scenarios are occurring,then the multiple demand surge module determines how to handlesatisfying the one or more resource demand conditions resulting from themultiple demand surge scenarios.

Accordingly, in various embodiments, one or more options may beavailable to handle the multiple demand surge scenarios that areoccurring at virtually the same time. In some embodiments, a firstoption may involve prioritizing the demand surge scenarios with respectto one another. In particular embodiments, a demand surge priority scoremay be generated for each demand surge scenario based at least in parton one or more parameters. For example, a healthcare service facilitymay use a parameters such as r² score that is a ratio of transmissionfor a disease to generate the demand surge priority score for aparticular demand surge scenario. While in another example, thehealthcare service facility may use another parameter such as mortalityrate associated with a disease. Such parameters may be gathered fromexpert organizations such as the World Health Organization (WHO) or maybe calculated by the healthcare service facility to reflect localregions. This first option may work well in cases where a first demandsurge scenario (e.g., an epidemic caused by a highly infectious virus)has a higher potential effect (e.g., a higher potential death rate) thanthe other demand surge scenario(s) so that satisfying the resourcedemand conditions for the first demand surge scenario is given priority.

A second option may involve processing the multiple demand surgescenarios simultaneously, without prioritizing one demand surge scenarioover another. This particular option may work well in instances wherethe demand surge scenarios are caused by external factors like, forexample, a terrorist attack, a fire in a national park, emergency in ahighly occupied building, and/or the like. Therefore, oftentimes forsuch scenarios, a parameter such as r2 or mortality rate does notnecessarily exist due to the external factors that can be used inprioritizing the demand surge scenarios.

Finally, a third option may involve merging the multiple demand surgescenarios by merging the resource demand conditions resulting from themultiple demand surge scenarios. The merged demand surge scenarios maythen be treated as a single demand surge scenario in identifyingresources to transform to satisfy the resource demand conditions for allof the demand surge scenarios. This option may work well when a goodnumber of the resource demand conditions for the multiple demand surgescenarios overlap. For example, multiple demand surge scenarios relatedto outbreaks of two different diseases resulting in a need for ICUservices.

Therefore, returning to FIG. 5 , the multiple demand surge moduledetermines whether to exercise the first option involving prioritizingthe multiple demand surge scenarios in Operation 515. Here, for example,the multiple demand surge module may be configured to evaluate whetherto use this option for the multiple demand surge scenarios based atleast in part on an indicator that is set identifying that the firstoption should be exercised for the multiple demand surge scenarios. Inother embodiments, the multiple demand surge module may be configured toevaluate one or more parameters and/or characteristics of the demandsurge scenarios in determining whether to exercise the first option. Forexample, the multiple demand surge module may determine whether themultiple demand surge scenarios involve infectious diseases or othermedical issues such as medical emergencies due to an event such as afire.

Therefore, if the multiple demand surge module determines the firstoption should be exercised for the multiple demand surge scenarios, thenthe multiple demand surge module prioritizes the demand surge scenariosin Operation 520. As previously noted, the multiple demand surge modulemay be configured in various embodiments to use demand surge priorityscores in prioritizing the multiple demand surge scenarios. Onceprioritized, the multiple demand surge module may generate and submitdemand surge requests for the demand surge scenarios according to thepriority in Operation 525. As discussed further herein, a demand surgerequest entails a request to have resources identified for the demandsurge scenarios that can be transformed to satisfy the resource demandconditions resulting from the demand surge scenario. As furtherdiscussed, such resources may be identified in various embodiments in anoptimized resource transformation scenario.

If the multiple demand surge module determines the first option shouldnot be exercised for the multiple demand surge scenarios, then themultiple demand surge module determines whether a second option shouldbe exercised for the multiple demand surge scenarios in Operation 530.Here, the second option involves processing the multiple demand surgescenarios simultaneously to identify the resources that can betransformed to satisfy the resource demand conditions for the demandsurge scenarios. Again, in particular embodiments, the multiple demandsurge module may be configured to evaluate whether to use the secondoption for the multiple demand surge scenarios based at least in parton, for example, whether an indicator has been set identifying theoption should be exercised or by evaluating one or more parametersand/or characteristics of the multiple demand surge scenarios.

Therefore, if the multiple demand surge module determines the secondoption should be exercised for the multiple demand surge scenarios, thenthe multiple demand surge module submits demand surge requests for thedemand surge scenarios simultaneously in Operation 535. As a result, anoptimized resource transformation scenario may be generated for eachdemand surge scenario identifying resources that can be transformed tosatisfy the resource demand conditions for the demand surge scenario.Here, in particular embodiments, the optimized resource transformationscenarios may be generated for the multiple demand surge scenarios withrespect to one another and the resource demand conditions that need tobe satisfied for the multiple demand surge scenarios. Therefore, if aparticular resource is identified in the optimized resourcetransformation scenario for a particular demand surge scenario, then theresource may not be identified, or be identified with a lower priority,in the optimized resource transformation scenario for a different demandsurge scenario.

Finally, if the multiple demand surge module determines that neither thefirst option nor the second option should be exercised for the multipledemand surge scenarios, then the multiple demand surge module exercisesthe third option by merging the demand surge scenarios in Operation 540.Here, the multiple demand surge module may carry out this particularoperation in various embodiments by merging the resource demandconditions for the different demand surge modules. This may involvecombining the resource demand conditions across the multiple demandsurge scenarios. Once combined, the multiple demand surge module submitsa demand surge request for the combined demand surge scenarios inOperation 545. As a result, the multiple demand surge scenarios may betreated as a single scenario in which a single optimized resourcetransformation scenario is generated to satisfy the resource demandconditions for all of the demand surge scenarios.

Demand Surge Rollback Module

As noted throughout, various embodiments of the disclosure involvegenerating an optimized resource transformation scenario for anidentified demand surge scenario in which the optimized resourcetransformation scenario identifies resources that may be transformed tosatisfy one or more resource demand conditions resulting from the demandsurge scenario. For example, a resource demand condition resulting froma demand surge scenario may involve the need for isolation space at ahealthcare service facility to be used for patients who are admitted tothe facility as a result of an epidemic. In this example, the optimizedresource transformation scenario may identify a room in the facilitythat is currently being used for office space that could be transformedinto isolation space for patients. Therefore, the healthcare servicefacility may transform the room accordingly so that the room may then beused for isolation space.

However, the epidemic (the demand surge scenario) causing the need forthe isolation space eventually subsides and the isolation space is nolonger needed. Therefore, in various embodiments, the demand surgescenario is monitored so that when the scenario appears to be coming toend, the resources may be rolled back to their previous use. That is tosay, in the example, the room may no longer be used for isolation space,and may be returned to being used for office space.

Therefore, turning now to FIG. 6 , additional details are providedregarding a process flow for rolling back a demand surge scenarioaccording to various embodiments. FIG. 6 is a flow diagram showing aDemand Surge Rollback Module for performing such functionality accordingto various embodiments of the disclosure. For example, the flow diagramshown in FIG. 6 may correspond to operations carried out by a processingelement 210 in a computing entity 200, such as an application server 110described in FIG. 1 , as it executes the Demand Surge Rollback Modulestored in the computing entity's volatile and/or nonvolatile memory.

The process flow 600 begins with the Demand Surge Rollback Modulequerying the resource demand indicators associated with the demand surgescenario in Operation 610. Therefore, in various embodiments, the DemandSurge Rollback Module queries data for the indicators from the resourcerepository. Such data may be similar to the data that was used inidentifying the demand surge scenario. Once queried, the Demand SurgeRollback Module generates one or more trends for the demand surgescenario in Operation 615. Accordingly, in particular embodiments, theDemand Surge Rollback Module may be configured to generate the one ormore trends for the demand surge scenario in the same manner as thedemand surge module previously discussed. However, in this instance, theDemand Surge Rollback Module is evaluating whether a downward trend isdeveloping with respect to the one or more resource demand conditionsassociated with the demand surge scenario. For instance, a “downwardtrend” may identify when the demand for one or more resources used tosatisfy the one or more resource demand conditions is diminishing.

Therefore, the Demand Surge Rollback Module determines whether thedownward trend satisfies a threshold in Operation 620. If so, then theDemand Surge Rollback Module submits a surge demand rollback request inOperation 625. As a result, a rollback resource transformation scenariomay be generated that identifies the resources that can be rolled backso that they may have the capacity to be used for the purpose intendedprior to being transformed to satisfy the one or more resource demandconditions resulting from the demand surge scenario. Thus, in theexample involving the isolation space in the healthcare servicefacility, the room transformed so that it could be used as isolationspace may be transformed back so that it may again be used for officespace.

Accordingly, in various embodiments, the Demand Surge Rollback Modulemay be configured so that the module is invoked periodically to evaluatethe surge demand scenarios that are currently occurring at a facility.For example, the Demand Surge Rollback Module may be involved daily toevaluate each surge demand scenario that is currently occurring todetermine whether a rollback process should be initiated for theresources transformed to address the surge demand scenario.

Dynamic Allocation Module

Once a demand surge scenario has been identified, various embodiments ofthe disclosure involve identifying one or more resources that can betransformed to satisfy one or more resource demand conditions resultingfrom the demand surge scenario. Here, an optimized resourcetransformation scenario may be generated that identifies the one or moreresources that can be transformed to satisfy the one or more resourceddemand conditions. Accordingly, the one or more resources may beidentified based at least in part on optimizing one or more parameterswith respect to the resources. For example, optimization may be carriedout in particular embodiments with respect to parameters such as cost oftransforming the resources, time required to transform the resources,opportunity cost associated with transforming the resources, and/or thelike.

Therefore, turning now to FIG. 7 , additional details are providedregarding a process flow for generating a optimized resourcetransformation scenario for a demand surge scenario according to variousembodiments. FIG. 7 is a flow diagram showing a dynamic allocationmodule for performing such functionality according to variousembodiments of the disclosure. For example, the flow diagram shown inFIG. 7 may correspond to operations carried out by a processing element210 in a computing entity 200, such as an application server 110described in FIG. 1 , as it executes the dynamic allocation modulestored in the computing entity's volatile and/or nonvolatile memory.

The process flow 700 begins with the dynamic allocation module receivinga demand surge request for a demand surge scenario in Operation 710.Here, in particular embodiments, the demand surge request may identifythe type and/or amount of resources need to satisfy the one or moreresource demand conditions resulting from the demand surge scenario, aswell as the time period over which the demand surge scenario is expectedto occur. In turn, the dynamic allocation module extracts resourcetransformation data objects from the resource repository for resourcesthat can be used to satisfy the one or more resource demand conditionsin Operation 715.

Accordingly, in some embodiments, the extracted resource transformationdata objects include objects representing resources that can be used tosatisfy the one or more resource demand conditions with and withouthaving to be transformed. For example, each of the resourcetransformation data objects may be one or more data entries found in theresource repository for resources that can be used to satisfy the one ormore resource demand conditions such as one or more data entries foundin the Facility Resources structure 310, Resource Demand Conditionsstructure 320, and/or Resource to Resource Demand Condition structure340 found in the data architecture 300 shown in FIG. 3 .

Here, in particular embodiments, the dynamic allocation module may beconfigured to extract the resource transformation data objects for thoseresources that can be transformed within the time period in which thedemand surge scenario is expected to occur. In addition, in someembodiments, the dynamic allocation module may be configured to useother criteria in extracting the resource transformation data objectsfor the resources such as, for example, the cost associated withtransforming the resources to be used to satisfy the one or moreresource demand conditions. Those of ordinary skill in the art canenvision other criteria that may be used in light of this disclosure.

The dynamic allocation module then identifies those resourcesrepresented by the extracted resourced data objects that are expected tobe underutilized during the demand surge scenario in Operation 720.Here, being “underutilized” indicates the resources are not expected tobe used to their full potential to satisfy other demand resourceconditions during the time period the demand surge scenario is expectedoccur. Depending on the embodiment, the dynamic allocation module may beconfigured to consider one or more conditions and/or parameters inevaluating whether a resource is expected to be used or not to theresource's full potential during the demand surge scenario.

For instance, the dynamic allocation module may evaluate the current usefor the resource in identifying whether the resource is expected to beused or not to the resource's full potential during the demand surgescenario. For example, the resource may be a nurse who is currentlyassigned to a maternity ward in a hospital. In this example, the demandsurge scenario may involve an epidemic that is expected to result in asignificant increase in the number of patients requiring care in the ICUunit at the hospital. The nurse may be scheduled to work fulltime in thematernity ward during the time period in which the epidemic is expectedto occur. However, although the nurse may not have any additionalcapacity to work in the ICU unit since he is scheduled to work fulltimein the maternity ward, the nurse may still be considered to beunderutilized during the time period that the epidemic is expected tooccur since the need for staff to work in the ICU unit is considered ahigher priority than staff to work the maternity ward. Therefore, thedynamic allocation module may recognize the nurse as a resource that isexpected to be underutilized during the time period the epidemic isexpected to occur. Those of ordinary skill in the art can recognizeother conditions and/or parameters that may be considered by the dynamicallocation module in identifying those resources that are expected to beunderutilized during the time period the demand surge scenario isexpected to occur in light of this disclosure.

Once the dynamic allocation module identifies those resources that areexpected to be underutilized during the demand surge scenario, thedynamic allocation module generates a downgrade-only resourcetransformation scenario in Operation 725. Accordingly, in variousembodiments, the downgrade-only resource transformation scenario mayidentify resource transformation data objects for those underutilizedresources that involve a downgrade transformation to be used to satisfythe one or more resource demand conditions. Here, in particularembodiments, the dynamic allocation module may be configured to evaluatethe resources based at least in part on one or more conditions and/orparameters to identify the downgrade set of resources.

For example, a resource may be considered as a downgrade resource if thecost associated with transforming the resource so that the resource maybe used to satisfy the one or more resource demand conditions is zero orsatisfies a cost threshold. For instance, if the cost to transform theresource is below a threshold amount. Other conditions and/or parametersmay be considered in identifying a downgrade resource such as timerequired for transformation, opportunity cost associated withtransforming the resources, and/or the like. Opportunity cost may be thecost associated with diverting the resource to be used to satisfy theone or more resource demand conditions resulting from the demand surgescenario from being used to satisfy one or more other resource demandconditions.

At this point, the dynamic allocation module determines whether theresources associated with the resource transformation data objectsidentified in the downgrade-only resource transformation scenariosatisfy the one or more resource demand conditions in Operation 730. Ifso, then the dynamic allocation module generates the optimized resourcetransformation scenario based at least in part on the downgrade-onlyresource transformation scenario in Operation 745.

However, if the resources associated with the resource transformationdata objects identified in the downgrade-only resource transformationscenario do not satisfy the one or more resource demand conditions, thenthe dynamic allocation module prioritizes the residual resources thatare not part of the resources represented by the downgrade-only resourcetransformation scenario in Operation 735. Accordingly, in variousembodiments, the dynamic allocation module performs this particularoperation by processing the resource transformation data objects for theresidual resources using a resource optimization machine learning model.

In various embodiments, the resource optimization machine learning modelis configured for optimizing the use of the residual resources requiringan upgrade transformation to address the one or more resource demandconditions. For instance, in some embodiments, the resource optimizationmachine learning model is a supervised or an unsupervised machinelearning model that is characterized by a set of resource optimizationparameters to generate one or more resource priority scores for theresidual resources. In other embodiments, the resource optimizationmachine learning model is a rule-based model that is characterized by aset of resource optimization parameters to generate one or more resourcepriority scores for the residual resources.

Depending on the embodiment, the set of resource optimization parametersmay involve, for example, a cost of transforming the resource to addressthe one or more resource demand conditions, a time required to transformthe resource to address the one or more resource demand conditions, anopportunity cost associated with transforming the resource to addressthe one or more resource demand conditions, and/or the like. Thus, theresource priority score for a residual resource may represent a priorityfor using the resource to address the one or more resource demandconditions with respect to the other residual resources. Accordingly, inparticular embodiments, the dynamic allocation module may then generatea hybrid resource transformation scenario based at least in part on theone or more resource priority scores for the residual resources and thedowngrade-only resource transformation scenario. Here, the hybridresource transformation scenario may identify a listing of the resourcetransformation data objects for the resources base on a priorityassociated with using each resource in satisfying the one or moreresource demand conditions.

At this point, the dynamic allocation module determines whether theresources associated with the resource transformation data objectsidentified in the hybrid resource transformation scenario satisfy theone or more resource demand conditions in Operation 740. If not, thenthe dynamic allocation module returns to Operation 715 and extractsresource transformation data objects for additional resources that canthen be used to satisfy the one or more resource demand conditions.Depending on the embodiment, the dynamic allocation module may beconfigured to use some type of criteria in extracting the resourcetransformation data objects for these additional resources. For example,the dynamic allocation module may be configured in some embodiments touse a cost and/or time threshold associated with transforming theseadditional resources to satisfy the one or more resource demandconditions. Those of ordinary skill in the art can envision variouscriteria that may be used in identifying the additional resources inlight of this disclosure.

However, if the resources associated with the resource transformationdata objects identified in the hybrid resource transformation scenariosatisfy the one or more resource demand conditions, then the dynamicallocation module generates the optimized resource transformationscenario in Operation 745 and outputs the scenario in Operation 750.Accordingly, in various embodiments, the optimized resourcetransformation scenario may then be used in performing one or moreresource transformation actions that involve transforming the resourcesassociated with the resource transformation data objects identified inthe scenario so that the resources may then be used to satisfy the oneor more resource demand conditions for the demand surge scenario. Forexample, the optimized resource transformation scenario may involve adata object such as a report that include information that can be usedin performing the one or more transformations actions such as type oftransformation needed for the resource (e.g., downgrade or upgrade), thecost of transformation, time needed for transformation, the priority fortransforming the resource, the resource demand condition(s) satisfied byresource, and/or the like.

In addition, the optimized resource transformation scenario may beconfigured in various embodiments so that the scenario may be used inperforming automated and/or manual transformation actions. For example,the optimized resource transformation scenario may be configured so thatone or more automated processes can use the scenario as input ingenerating instructions for transforming the resources. While in otherinstances, the optimized resource transformation scenario may be used ina decision support role providing recommendations to personnel such asfacility administrator(s) who make decisions in resource allocation toaddress the demand surge scenario. Thus, the optimized resourcetransformation scenario may ensure resources can be transformed tosatisfy the one or more resource demand conditions resulting from thedemand surge scenario with the lowest time and/or cost (e.g., accordingto an optimized solution). In some embodiments, the dynamic allocationmodule may be configured to present user interface data for a predictionoutput user interface that describes the optimized resourcetransformation scenario and transmit the user interface data to a clientcomputing entity, where the client computing entity may be configured topresent the prediction output user interface based at least in part onthe user interface data. An operational example of a prediction outputuser interface 1100 is depicted in FIG. 11 .

Resource Rollback Module

As previously noted, a rollback of resources that have been transformedto satisfy one or more resource demand conditions resulting from ademand surge scenario may be performed in various embodiments once thedemand surge scenario is or has ended. Therefore, turning now to FIG. 8, additional details are provided regarding a process flow for rollingback one or more resources so that they may be returned to theiroriginal use according to various embodiments. FIG. 8 is a flow diagramshowing a resource rollback module for performing such functionalityaccording to various embodiments of the disclosure. For example, theflow diagram shown in FIG. 8 may correspond to operations carried out bya processing element 210 in a computing entity 200, such as anapplication server 110 described in FIG. 1 , as it executes the resourcerollback module stored in the computing entity's volatile and/ornonvolatile memory.

The process flow 800 begins with the resource rollback module receivinga surge demand rollback request for a particular demand surge scenarioin Operation 810. In response, the resource rollback module retrievesthe optimized resource transformation scenario for the demand surgescenario in Operation 815. Accordingly, the optimized resourcetransformation scenario identifies one or more resource transformationdata objects associated with resources that may have been transformed tosatisfy one or more resource demand conditions that resulted from thedemand surge scenario. Therefore, the resource rollback module may usethe optimized resource transformation scenario in generating a plan forrolling back such resources so that they may be used for their originalpurposes.

Thus, in various embodiments, the resource rollback module queries theoriginal uses of the resources in Operation 820. Here, in particularembodiments, the resource rollback module may query information on theoriginal uses from the resource repository. Once queried, the resourcerollback module then generates a resource rollback scenario and outputsthe scenario in Operations 825 and 830. Accordingly, the resourcerollback scenario may be used in various embodiments in a similar manneras the optimized resource transformation scenario. Therefore, theresource rollback scenario may be used in performing one or morerollback actions that involve transforming the resources associated withthe resource transformation data objects identified in the resourcerollback scenario so that the resources may again be used to satisfy oneor more resource demand conditions as originally intended.

CONCLUSION

Many modifications and other embodiments of the disclosure set forthherein will come to mind to one skilled in the art to which thesemodifications and other embodiments pertain having the benefit of theteachings presented in the foregoing descriptions and the associateddrawings. Therefore, it is to be understood that the disclosure is notto be limited to the specific embodiments disclosed and thatmodifications and other embodiments are intended to be included withinthe scope of the appended claims. Although specific terms are employedherein, they are used in a generic and descriptive sense only and notfor purposes of limitation.

The invention claimed is:
 1. A computer-implemented method comprising:identifying, by one or more processors, a demand surge scenario, wherein(a) the demand surge scenario is associated with a resource demandcondition and (b) identifying the demand surge scenario comprises: (i)generating, using a trend surge prediction machine learning model, atrend surge prediction based at least in part on resource demand data,wherein the trend surge prediction indicates a likelihood of a currentdemand surge; (ii) generating, using a historical surge predictionmachine learning model, a historical surge prediction based at least inpart on cyclical trend data, wherein the historical surge predictionindicates a likelihood of a historical demand surge; (iii) generating asurge score based at least in part on the trend surge prediction and thehistorical surge prediction; and (iv) responsive to the surge scoresatisfying a surge score threshold, identifying the demand surgescenario; responsive to identifying the demand surge scenario: (a)determining, by the one or more processors and based at least in part onresource transformation data associated with a plurality of resources, adowngrade set of the resources; b) determining, by the one or moreprocessors, whether a downgrade-only resource transformation scenariosatisfies the resource demand condition; (c) responsive to determiningthat the downgrade-only resource transformation scenario fails tosatisfy the resource demand condition: (i) identifying, by the one ormore processors, a residual resource of the resources (1) that istransformable to satisfy the resource demand condition and (2) that isnot in the downgrade set; (ii) generating, by the one or moreprocessors, a resource priority score for the residual resource; (iii)identifying, by the one or more processors, a hybrid resourcetransformation scenario based at least in part on the resource priorityscore and the downgrade-only resource transformation scenario; and (iv)identifying, by the one or more processors, an optimized resourcetransformation scenario based at least in part on the hybrid resourcetransformation scenario; and generating and providing, by the one ormore processors, a recommendation for one or more resourcetransformation actions to be performed based at least in part on theoptimized resource transformation scenario.
 2. The computer-implementedmethod of claim 1, wherein the resource transformation data comprisesdata associated with (a) the resources in the downgrade set or (b) theresidual resource that is transformable to satisfy the resource demandcondition within an expected time occurrence for the demand surgescenario.
 3. The computer-implemented method of claim 1, wherein theresources in the downgrade set are transformable to satisfy the resourcedemand condition for no cost or a cost satisfying a threshold cost. 4.The computer-implemented method of claim 1, wherein the resourcetransformation data comprises a set of resource optimization parametersthat comprises at least one of a transformation cost parameter or atransformation time parameter.
 5. The computer-implemented method ofclaim 1, wherein the one or more resource transformation actionscomprise executing an operation on the residual resource to satisfy theresource demand condition.
 6. The computer-implemented method of claim1, wherein generating the surge score based at least in part on thetrend surge prediction and the historical surge prediction comprisescombining the trend surge prediction and the historical surgeprediction.
 7. The computer-implemented method of claim 5, wherein thedemand surge scenario is an event that results in a markedly increase ina need for the residual resource.
 8. An apparatus comprising one or moreprocessors and memory comprising program code, the memory and theprogram code configured to, with the one or more processors, cause theapparatus to at least: identify a demand surge scenario, wherein (a) thedemand surge scenario is associated with a resource demand condition and(b) identifying the demand surge scenario comprises: (i) generating,using a trend surge prediction machine learning model, a trend surgeprediction based at least in part on resource demand data, wherein thetrend surge prediction indicates a likelihood of a current demand surge;(ii) generating, using a historical surge prediction machine learningmodel, a historical surge prediction based at least in part on cyclicaltrend data, wherein the historical surge prediction indicates alikelihood of a historical demand surge; (iii) generating a surge scorebased at least in part on the trend surge prediction and the historicalsurge prediction; and (iv) responsive to the surge score satisfying asurge score threshold, identifying the demand surge scenario; responsiveto identifying the demand surge scenario: (a) determine, based at leastin part on resource transformation data associated with a plurality ofresources, a downgrade set of the resources; b) determine whether adowngrade-only resource transformation scenario satisfies the resourcedemand condition; (c) responsive to determining that the downgrade-onlyresource transformation scenario fails to satisfy the resource demandcondition: (i) identify a residual resource of the resources (1) that istransformable to satisfy the resource demand condition and (2) that isnot in the downgrade set; (ii) generate a resource priority score forthe residual resource; (iii) identify a hybrid resource transformationscenario based at least in part on the resource priority score and thedowngrade-only resource transformation scenario; and (iv) identify anoptimized resource transformation scenario based at least in part on thehybrid resource transformation scenario; and generate and provide arecommendation for one or more resource transformation actions to beperformed based at least in part on the optimized resourcetransformation scenario.
 9. The apparatus of claim 8, wherein theresource transformation data comprises data associated with (a) theresources in the downgrade set or (b) the residual resource that istransformable to satisfy the resource demand condition within anexpected time occurrence for the demand surge scenario.
 10. Theapparatus of claim 8, wherein the resources in the downgrade set areeach transformable to satisfy the resource demand condition for no costor a cost satisfying a threshold cost.
 11. The apparatus of claim 8,wherein the resource transformation data comprises a set of resourceoptimization parameters that comprises at least one of a transformationcost parameter or a transformation time parameter.
 12. The apparatus ofclaim 8, wherein the one or more resource transformation actionscomprise executing an operation on the residual resource to satisfy theresource demand condition.
 13. The apparatus of claim 8, whereingenerating the surge score based at least in part on the trend surgeprediction and the historical surge prediction comprises combining thetrend surge prediction and the historical surge prediction.
 14. Theapparatus of claim 12, wherein the demand surge scenario is an eventthat results in a markedly increase in a need for the residual resource.15. A non-transitory computer storage medium comprising instructionsconfigured to cause one or more computer processors to at least performoperations configured to: identify a demand surge scenario, wherein (a)the demand surge scenario is associated with a resource demand conditionand (b) identifying the demand surge scenario comprises: (i) generating,using a trend surge prediction machine learning model, a trend surgeprediction based at least in part on resource demand data, wherein thetrend surge prediction indicates a likelihood of a current demand surge;(ii) generating, using a historical surge prediction machine learningmodel, a historical surge prediction based at least in part on cyclicaltrend data, wherein the historical surge prediction indicates alikelihood of a historical demand surge; (iii) generating a surge scorebased at least in part on the trend surge prediction and the historicalsurge prediction; and (iv) responsive to the surge score satisfying asurge score threshold, identifying the demand surge scenario; responsiveto identifying the demand surge scenario: (a) determine, based at leastin part on resource transformation data associated with a plurality ofresources, a downgrade set of the resources; (b) determine whether adowngrade-only resource transformation scenario satisfies the resourcedemand condition; (c) responsive to determining that the downgrade-onlyresource transformation scenario fails to satisfy the resource demandcondition: (i) identify a residual resource of the resources (1) that istransformable to satisfy the resource demand condition and (2) that isnot in the downgrade set; (ii) generate a resource priority score forthe residual resource; (iii) identify a hybrid resource transformationscenario based at least in part on the resource priority score and thedowngrade-only resource transformation scenario; and (iv) identify anoptimized resource transformation scenario based at least in part on thehybrid resource transformation scenario; and generate and provide arecommendation for one or more resource transformation actions to beperformed based at least in part on the optimized resourcetransformation scenario.
 16. The non-transitory computer storage mediumof claim 15, wherein the resource transformation data comprises dataassociated with (a) the resources in the downgrade set or (b) theresidual resource that is transformable to satisfy the resource demandcondition within an expected time occurrence for the demand surgescenario.
 17. The non-transitory computer storage medium of claim 15,wherein the resource transformation data comprises a set of resourceoptimization parameters that comprises at least one of a transformationcost parameter or a transformation time parameter.
 18. Thenon-transitory computer storage medium of claim 15, wherein theresources in the downgrade set are each transformable to satisfy theresource demand condition for no cost or a cost satisfying a thresholdcost.
 19. The non-transitory computer storage medium of claim 15,wherein the one or more resource transformation actions compriseexecuting an operation on the residual resource to satisfy the resourcedemand condition.
 20. The non-transitory computer storage medium ofclaim 15, wherein generating the surge score based at least in part onthe trend surge prediction and the historical surge prediction comprisescombining the trend surge prediction and the historical surgeprediction.